(1) Field of the Invention
The present invention relates to a method for detecting signals in noise. More specifically, the present invention relates to the separation of known or unknown time series data into its narrowband and broadband components.
(2) Description of the Prior Art
Many applications require the identification of a desired signal within undesired random signals (noise) which is received with, and often interferes with, the desired signal. For example, in sonar systems randomly generated sounds from both natural and man-made sources give rise to a noise that interferes with desired acoustic signals. The detection and identification of a specific target, such as an underwater vehicle, requires a system which can detect a signal corresponding to the target within received data containing both the signal and noise.
Underwater acoustic signals are often complicated, consisting of a superposition: ##EQU1##
of non-stationary narrowband (hereinafter designated as NB) n.sub.k (t) and broadband (hereinafter designated as BB) b(t) components. Such signals arise from a variety of sources, such as ship machinery, marine mammals, drilling platforms, and active sonars. FIG. 1 illustrates what a typical underwater acoustic spectrum might look like, consisting of a superposition of narrowband line-like components plus a broadband component. The BB component itself can be colored, with many local spectral peaks and valleys.
There is a need for the separation of the signal x(t) into the constituent NB:n.sub.k (t)=S.sup.p.sub.k=1 n.sub.k (t) and BB:b(t) time series components when little or nothing is known about the NB and BB components and only a short data record is available. This is of great importance in sonar, especially passive sonar, where the desirable signal (for detection, classification, and localization) is often either the NB or BB component and the other is regarded as interference.
Existing approaches primarily deal with power spectrum estimation of the composite data, rather than recovering the constituent narrowband and broadband time series. Having the separated narrowband and broadband time series available is very useful since it allows many additional forms of processing (processing which is impossible or difficult to do using only the power spectrum), such as extraction of time series statistics, improved wavelet and Wigner analysis, pattern recognition, and parametric modeling.
The separation of the time series data into the NB and BB time series components is difficult. Wiener filtering (see S. Haykin, "Adaptive Filter Theory, Third Edition", Prentice Hall, 1996) is not practical since the covariance or spectral densities of the NB and BB components are not known. Parametric methods, e.g., MA (Moving Average), AR (Auto Regressive), ARMA (Auto Regressive Moving Average) modeling (see P. Stoica et al., "Introduction to Spectral Analysis", Prentice Hall, 1997), require choosing a model type for the underlying broadband component and for each of the NB components present. This is difficult since nothing is known about the NB and BB components. Adaptive methods applied directly to the time series, such as adaptive notch filters and line enhancers (see S. Haykin, supra) and Principal Component Inverse (PCI) method (see D. Tufts et al., "Data Adaptive Estimation by Singular-Value Decomposition of a Data Matrix" Proc. IEEE, Vol. 7, pp. 684-685, 1982; I. P. Kirsteins et al., "Adaptive Detection Using Low Rank Approximation to a Data Matrix" IEEE, Trans. Aerospace and Elect. Sys., Vol. 30, No. 1, pp. 55-57, 1994), tend to perform poorly when the broadband spectrum has a large dynamic range. That is, if a weak NB component is present in a "valley" of the BB spectrum, the notch filter tracker or PCI method might lock onto a nearby peak of the BB spectrum and filter it as the NB component, rather than the true NB component.